Multidimensional databases are generally superior to traditional relational database management systems in terms of speed, size, and manageability. Conceptually in multidimensional database systems, data is represented as cubes with a plurality of dimensions, rather than relational tables with rows and columns. A cube includes groups of data that are located at three or more dimensions. Dimensions are cube attributes used to locate and organize the data in the cube. Each dimension has a hierarchy of levels of dimension members. Data in the cube can be aggregated based on the hierarchy. Accordingly, data can be viewed at different levels of details. The multidimensional model is optimized to handle large amounts of data. In particular, it allows users to execute complex queries on data cubes for analyzing business information.
As the global business climate becomes ever more competitive, survival in the marketplace requires timely and precise business decisions based on accurate and up-to-date information. Data analysis, reporting, and database query software provide business users with the tools to process the ever-growing mountain of data. Business intelligence (BI) is the name given to the broad category of applications and technologies for helping business users make better decisions. BI applications include decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining. For decision support and OLAP applications, multidimensional databases offer improvements over conventional relational databases in calculation performance, trend analysis/modeling, business modeling capabilities, and the management of sparse data sets. The structure of a multidimensional database is superior to a relational database for these applications because the sophisticated aggregation paths, calculations, and write-back capabilities can help model, more effectively, the business entities and relationships among entities that make up a company's operations. For example, companies usually organize products by lines or families, customers by regions or distribution channels, and employees by divisions and regions. Analysts use these structures which are naturally implemented in multidimensional databases to navigate the data in an intuitive manner. Multidimensional databases are so commonly used for OLAP applications that OLAP is almost synonymous with multidimensional databases.
Users of sophisticated BI systems may wish to extract data from one database, perform transformations of the extracted data, and load the transformed data into another database. This process (referred to as “ETL”—Extraction, Transformation, and Loading) allows for the consolidation of data into a centralized data warehouse. This consolidated data may be used with one of the many database analysis, data mining, reporting and visualization tools. Businesses typically manage their data warehouse using relational database management systems. Many business intelligence reporting, analysis, and visualization applications are designed to run on data from this type of relational database. Relational database technology differs from multidimensional data stores. Users wishing to employ relational database reporting, analysis, and visualization tools with data originating from a multidimensional database require that the data and metadata (i.e. the descriptive information about the data contained in the database) be extracted from the multidimensional database. For instance, ETL is commonly performed by extracting data from a source multidimensional database, and by supplying the extracted data to a destination database that is optimized to support analysis and reporting, typically a conventional relational database.